[Submitted on 29 Jul 2026]
Title:HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs
View a PDF of the paper titled HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs, by Ru Peng and 11 other authors
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Abstract:High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the humanities and social sciences (HSS) are overlooked, and their open-ended nature makes synthesis challenging. Moving beyond prior capability-centric, fragmented attempts, we adopt a subject-centric paradigm, define the first HSS domain system covering 14 mainstream fields, and introduce HSS-Synth, the first data synthesis pipeline for HSS. HSS-Synth comprises: (1) constructing seed documents from web corpora via multi-step filtering and text refinement evaluated by a judge; (2) specifying "requirements + persona" to backtranslate seed documents into diverse yet faithful instructions with a strict Q&A alignment check; and (3) breaking LLM response limits via teacher-forced Answering that feeds seed documents during response generation to anchor semantics, reduce hallucinations, and preserve tone and integrity. HSS-Synth yields 237k high-quality, diverse instruction-tuning samples that outperform 14 leading baselines on 16 benchmarks. The fine-tuned Qwen3-8B-Base sets a new SOTA and approaches the official Qwen3-8B, improving both human preference and knowledge capabilities without performance seesaws. Extensive experiments demonstrate HSS-Synth's robustness and transferability. Our code is publicly available at this https URL.
Comments: ACL Findings 2026 Paper
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.27379 [cs.CL]
(or arXiv:2607.27379v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.27379
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ru Peng [view email] [v1] Wed, 29 Jul 2026 18:37:14 UTC (16,109 KB)
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